System and method for detecting an anomalous condition in a multi-step process

ABSTRACT

A technique for analyzing an anomalous condition in a process for producing a product is described, where the process includes plural subprocesses for performing operations on the product. The technique includes: (a) for each of the subprocesses, providing sensor output from at least one sensor used to measure information pertaining to the status of the respective subprocess; (b) for each of the subprocesses, extracting at least one representative value that is characteristic of a pattern expressed in the output, thus generating a plurality of representative values for the process as a whole; (c) retrieving data from a knowledge base, the data including a plurality of representative values, and also including information which maps the representative values to associated anomalous conditions; (d) analyzing the plurality of representative values output from the parameter extracting step with respect to the data stored in the knowledge base, and for generating a diagnostic result which diagnoses an anomalous condition in the process, and also identifies at least one of the subprocesses which has caused the anomalous condition; and (e) using the diagnostic result to affect corrective action to the at least one of the subprocesses which has caused the anomalous condition by adjusting at least one actuator that controls the at least one subprocess.

BACKGROUND OF THE INVENTION

The present invention generally relates to a system and method fordetecting an anomalous condition. In a more specific embodiment, thepresent invention relates to a system and method for detecting ananomalous condition which occurs in the production of a manufacturedgood.

Modern manufacturing plants produce products using a complex series ofoperations. The manufacturing plants generally rely on electronicequipment to govern these operations. For instance, in a typical plant,a computer equipment is used to transmit instructions to machines usedto manufacture a product. Further, the computer equipment receivesinformation collected from sensors interspersed throughout the process.These sensors collect information regarding the status of the machinesand the quality of products processed by the machines.

The machines and other equipment used in the manufacturing plantoccasionally function in a substandard manner, as manifested, forexample, in the generation of out-of-tolerance products. To address thisproblem, many manufacturing plants employ an expert who examines theoutput of the sensors. The expert may generate a hypothesis regardingthe cause of an anomaly based on his or her analysis of the output ofthe sensors. The expert's judgment is typically based on his or herprior encounters with similar failure conditions. After forming aconclusion, the expert instructs the personnel operating the plantmachines to make one or more adjustments to correct the anomalouscondition.

Such a technique has drawbacks. Even experts are subject to errors injudgment. Accordingly, the expert may misdiagnose the cause of theanomalous condition, and/or provide incorrect instructions for remedyingthe problem. This may require the expert to make another visit to theplant, reanalyze the sensor outputs, and make another diagnosis.

Further, some industries may have relatively few individuals that arequalified to diagnose the failure conditions in the manufacturing plant.The scarcity of experts may result in a situation where an expert is notimmediately available to analyze the cause of the anomaly.

Both of the above difficulties may result in delays in production and/orthe production of substandard goods. This may lead to a possible loss ofrevenue for the manufacturing plant. In addition, if such an expert isnot employed by the company running the manufacturing plant, the companymust pay for the services of the expert.

There is accordingly a need for a more satisfactory system and methodfor diagnosing the cause of anomalies in the production of goods, andfor providing appropriate corrective action.

BRIEF SUMMARY OF THE INVENTION

A technique is disclosed for analyzing an anomalous condition in aprocess for producing a product, where the process includes pluralsubprocesses for performing operations on the product. The techniqueincludes: (a) for each of the subprocesses, providing sensor output fromat least one sensor used to measure information pertaining to the statusof the respective subprocess; (b) for each of the subprocesses,extracting at least one representative value that is characteristic of apattern expressed in the output, thus generating a plurality ofrepresentative values for the process as a whole; (c) retrieving datafrom a knowledge base, the data including a plurality of representativevalues, and also including information which maps the representativevalues to associated anomalous conditions; (d) analyzing the pluralityof representative values output from the parameter extracting step withrespect to the data stored in the knowledge base, and for generating adiagnostic result which diagnoses an anomalous condition in the process,and also identifies at least one of the subprocesses which has causedthe anomalous condition; and (e) using the diagnostic result to affectcorrective action to the at least one of the subprocesses which hascaused the anomalous condition by adjusting at least one actuator thatcontrols the at least one subprocess.

The use of parameter extracting and pattern recognition steps toautomatically analyze anomalies provides a more efficient andcost-effective solution for diagnosing anomalies (compared to exclusiveuse of human analysis). For instance, the above-described solutioneliminates the need for the local site to retain the services of a humanexpert to perform failure diagnosis for a manufacturing process.

Still further features and advantages of the present invention areidentified in the ensuing description, with reference to the drawingsidentified below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an exemplary system for implementing the present invention.

FIG. 2 shows an exemplary process for manufacturing products, where theprocess includes multiple steps, each of which may include multiplesubsteps.

FIG. 3 shows an example of a cold-rolling process, and associatedtension sensors used to monitor the process.

FIG. 4 shows another example of a cold-rolling process, and associatedx-ray sensors used to monitor the process.

FIG. 5 shows logic for analyzing an anomaly.

FIG. 6 shows a table for storing information extracted from sensor datafor a plurality of products produced in a process.

FIG. 7 shows an example of information obtained from sensors used tomonitor a cold rolling process, along with diagnostic informationassociated with the sensor outputs.

FIG. 8 shows a flowchart for explaining an exemplary process foradjusting a control model.

FIG. 9 shows an exemplary routine for analyzing an anomaly.

DETAILED DESCRIPTION OF THE INVENTION

The term “products” used herein refers to any type of products producedby any type of process and/or machine (or series of machines). In a moreparticular embodiment, the products pertain to goods produced inmultiple stages, such as paper goods or metal goods. Such goods areproduced by a generally continuously running process, and then typicallyseparated and sold as discrete products. For instance, in the case ofthe production of paper and metal goods, a manufacturing plant mayproduce multiple sheets or rolls of such material for shipment toconsumers.

FIG. 1 shows an exemplary system 100 for implementing the presentinvention. By way of overview, the system 100 includes a controlledprocess 102 coupled to a local processing system 104. A packet network108 couples the local processing system 104 to a remote system 110. Theremote system 110 provides remote control of the controlled process 102via the packet network 108 and local processing system 104. Although notshown, the remote system 110 may be coupled to multiple controlledprocesses and associated local processing systems. The followingdiscussion provides additional detail regarding each of theabove-identified features.

The controlled process 102 generically represents any equipment used tomanufacture a product. For instance, although not specificallyillustrated, the controlled process 102 may include multiple machinesfor carrying out operations on goods in a series of stages. Suchmachines may include various actuators for governing the operation ofthe machines, such as actuators for opening and closing valves,adjusting the speed of moving parts, controlling the temperature or gaspressure in the machines, etc. In addition, the controlled process 102may include one or more local control units for providing local controlof the machines used in the process.

The controlled process 102 further includes various sensors forcollecting information regarding the performance of the machines used inthe process, such as various x-ray sensors for measuring productthickness, tension sensors, temperature sensors, etc.

The local control system 104 generically represents equipment used todirectly interact with the controlled process 102. For instance, thelocal processing system 104 may include control equipment that islocated at the same facility (e.g., the same mill site) as thecontrolled process 102. Alternatively, the local system 104 may belocated at a facility nearby the mill site, or otherwise closelyassociated with the mill site.

The local control system 104 includes a historian 112. The historian 112comprises a data management unit that receives information from thecontrolled process 102 (such as information received from the sensorsused to monitor the process 102). Such data may be transferred to alogged data file 114 for archiving, and/or may be processed to generateone or more reports 116, e.g., on a periodic basis.

The local system 104 may also include a reference distributor 132. Thedistributor 132 forwards a control model to the controlled process 102,where it is used to configure local controllers used in the process 102.As discussed in the Background section, a typical control model definesa relationship for controlling the process as a function of prevailingconditions in the process. For instance, an exemplary control model maydefine a set of reference points that configure the actuators tofunction in a prescribed manner. These reference points define a“recipe” used for controlling the actuators based on a particularcondition that is prevailing in the controlled process 102. The localprocessing system 104 further includes a recipe box 134 for storing oneor more recipes for use in controlling the process 102.

The historian 112 and reference distributor 132 may comprise discretelogic for performing the above-described functions. Alternatively, thehistorian 112 and reference distributor 132 may comprise computer unitsincluding conventional computer hardware (not shown), such as aprocessor (e.g., a microprocessor), Random Access Memory (RAM), ReadOnly Memory (ROM), etc. Software functionality may be stored in suchcomputer units to program these units to perform the above-describedtasks.

The local processing system 104 may also include a local network 128 forcoupling various modules included within the local processing system104. The local network 128 may also couple the modules contained in thelocal processing system 104 to the control units and other functionalitycontained within the controlled process 102. Such a network 128 maycomprise a local area network (LAN), or some of other type of network.

The network 128 may also interact with various other units, such as theprimary data input unit 130. The primary data input unit 130 serves as aportal for receiving scheduling orders that will govern the operation ofthe controlled process 102 from a high-level perspective. The primarydata input unit 130 may also serve as a portal for interfacing withvarious user workstations. Such workstations may be manned by personnelwho are monitoring the process 102 and wish to make manual adjustmentsto the process 102 based on their assessment of anomalies in the processor other perceived events.

The network 108 couples the local processing system 104 with the remoteprocessing system 110. More specifically, the local processing system104 may forward information collected via the historian 112 to theremote system 110 via the network 108. Further, the local processingsystem 104 may receive information from the remote system 110 via suchnetwork 108. More specifically, as will be explained in greater detailbelow, the remote system 110 receives information regarding sensedconditions in the controlled process 102 via the local processing system104. On the basis of this information, the remote system 110 generates arecipe for use by the controlled process 102 in controlling itsactuators. This recipe is transmitted to the controlled process 102 viathe reference distributor 132 of the local processing system 104.

In a preferred embodiment, the network 108 comprises a wide-area network(WAN) supporting TCP/IP packet traffic (i.e., Transmission ControlProtocol/Internet Protocol traffic). In a more specific preferredembodiment, the network 108 comprises the Internet or an intranet, etc.In other applications, the network 108 may comprise other types ofnetworks governed by other types of protocols.

The network 108 may be formed, in whole or in part, from hardwiredcopper-based lines, fiber optic lines, wireless connectivity, etc.Further, the network 108 may operate using any type of network-enabledcode, such as HyperText Markup Language (HTML), Dynamic HTML, ExtensibleMarkup Language (XML), Extensible Stylesheet Language (XSL), DocumentStyle Semantics and Specification Language (DSSSL), Cascading StyleSheets (CSS), etc.

In terms of architecture, the remote system 110 may be formed as aconventional server (e.g., in the context of the well knownclient-server architecture). In an alternative embodiment, the remotesystem 110 may be implemented using an architecture other than aclient-server type architecture. For instance, the remote system 110 maybe implemented using a mainframe-type architecture. In one embodiment,the remote system 110 comprises a single computer. Alternatively, theremote system 110 may comprise multiple computers connected together ina distributed fashion, each of which may implement/administer a separateaspect of the functions performed by the remote system 110.

More specifically, the remote system 110 may include conventionalhead-end components, including a processor 120 (such as amicroprocessor), memory 123, cache (not shown), communication interface118, and database 121. The processor 120 serves as a central engine forexecuting machine instructions. The memory 123 (such as a Random AccessMemory, or RAM, etc.) serves the conventional role of storing programcode and other information for use by the processor 120. Thecommunication interface 118 serves the conventional role of interactingwith external equipment, such as the local system 104 via the network108. The database (or data warehouse) 121 serves as a central repositoryfor storing information collected from the local processing system 104,as well as other information. Generally, such a database 121 maycomprise a single repository of information. Alternatively, the database121 may comprise multiple repositories of information coupled to eachother in a distributed fashion. A variety of different databaseplatforms can be used to implement the database, including Oracle™relational database platforms sold commercially by Oracle Corp. Otherdatabase platforms, such as, Microsoft SQL™ server, Informix™, DB2(Database 2), Sybase, etc., may also be used.

The remote system 110 may include general purpose operating software forperforming its ascribed server functions. For instance, the remotesystem 110 may operate using any one of various operating systemplatforms, such as Microsoft Windows™ NT™, Windows™2000, Unix, Linux,Xenix, IBM AIX™, Hewlett-Packard UX™, Novell Netware™, Sun MicrosystemsSolaris™, OS/2™, BeOS™, Mach, Apache, OpenStep™ or other operatingsystem or platform.

The remote system 110 may also comprise processing functionality 122.Such processing functionality 122 may represent machine-readableinstructions for execution by the processor 120 for carrying out variousapplication-related functions. Such machine-readable instructions may bestored in any type of memory, such as magnetic media, CD ROM, etc. In analternative embodiment, such functionality 122 may be implemented asdiscrete logic circuitry (e.g., as housed on special computer cards thatplug into the remote system 110 in a conventional fashion).

The functionality 122 may include a number of modules used to generateoutput which governs the controlled process 102. For instance, thefunctionality 122 may include control model adjustment logic 124 thatexamines information regarding the operating conditions in thecontrolled process 102. From that information, the adjustment logic 124determines what control model is best suited to control the process. Forinstance, the adjustment logic 124 may store various algorithms whichcompute a recipe or adaptation to a recipe previously stored in thelocal recipe box, 134 based on prevailing sensed conditions in thecontrolled process 102. Alternatively, the adjustment logic 124 mayinclude a table lookup mechanism which determines a recipe based onprevailing sensed conditions. The specific approach used by theadjustment logic 124 depends on the nature of the process beingcontrolled. Software programs for performing such control are generallycommercially available, such as, GE Fanuc Cimplicity™ HMI (Human MachineInterface)

The remote system 110 forwards a calculated recipe or recipe adaptationto the local processor 104. The reference distributor 132 of the localprocessor 104 then forwards the recipe to the control units of thecontrolled process 102. In addition, the remote system 110 may alsostore the recipe in the local recipe box 134. The local recipe box 134may be used to furnish recipes in the event that the local system 104cannot access the remote system 110 via the network 108 (e.g., becauseof a failure in the network 108 or in the remote system 110). Therecipes retrieved from the local recipe box 134 may not be optimallysuited to the prevailing process conditions. Nevertheless, these recipesmay allow for the production of goods within prescribed tolerances. Aplant operator may decide to use such non-optimal recipes because thisoption is deemed more cost-effective than stopping operation in themanufacturing plant.

FIG. 2 generically shows the controlled process 102. The process 102includes plural principal processing steps, e.g., steps 202, 204, 206,and 208. Further, each of the principal steps may include pluralsub-processing steps associated therewith. In the exemplary case of FIG.2, for instance, principal step 204 includes subprocessing steps 210,212, 214, and 216.

For example, the production of steel includes plural principal steps.Well-known exemplary steps include continuous casting (or some othermethod of steel production, such as conventional ingot teeming, etc.),hot strip processing, pickling and oiling processing, cold stripprocessing, annealing, temper rolling galvanizing, etc.

Continuous casting provides a technique for transforming steel from itsmolten state into blooms, ingots, or slabs. In this technique, moltenmetal is poured into molds. From there, the metal advances down througha series of water-cooled rollers. Another group of guide rollers mayfurther transform the steel into a desired shape.

Hot rolling provides a technique for further shaping the steel. In thistechnique, a reheat furnace may be used to reheat the steel slabs. Thehot rolling technique then passes the slabs through a succession ofmills, including, for instance, a blooming mill, a roughing mill, and afinishing mill. These mills progressively reduce the thickness of themetal product. In a final stage, the hot rolling technique rolls thesteel into a coil. Mill operators may then transport these coils toother stations for further processing.

A layer of oxides typically forms on the surface of the metal stripduring the hot rolling process. This layer is referred to as “scale.”The pickling process provides a technique for removing this deleteriouslayer using an acid. Further, the pickling technique may apply a pickleoil to the surface of the strip to facilitate subsequent cold rollingoperations. In a common implementation, pickling procedures are carriedout in multiple stages, including an entry stage, scale removal stage,and pickling and exit stages. The entry stage typically includes amechanism for conveying the coil, a mechanism for uncoiling the coil,and a welder for welding the tail of one coil to the head of another (toprovide continuous processing of the rolls). The scale removal stage mayinclude a mechanism for tensioning the strip, storing the strip (e.g.,using a looping pit, etc.), and a temper mill to remove scale that formson the strip surface. The pickling and exit stage may employ acid andrinse tanks to apply acid to the strip, a mechanism for accumulatingstrip, a mechanism for oiling the strip with the pickling oil, and amechanism for coiling the strip. In a common implementation, thepickling technique uses the following chemical reaction to remove scalefrom the surface of the metal strip: HCl+FeO=H20+FeCl2.

The cold rolling process involves performing a series of operations onthe strip of steel at ambient temperature. Namely, this techniqueinvolves uncoiling the strip of metal, passing the strip through aseries of rolling stands to successively reduce its thickness, and thenrecoiling the strip. Each of the stands uses a series of rollers,including two opposing working rollers defining a gap therebetween.Thickness reduction is achieved by successively narrowing the gap in theseries of the stands. This technique further involves spraying alubricating liquid onto the surface of the strip as it passes throughthe cold rolling mill (e.g., comprising a mixture of water and oil). Thecold rolling procedure requires coordinated control of the stands. Thisis achieved through a collection of x-ray thickness sensors, tensionsensors, and automatic gauge control devices (to be described in greaterdetail in the context of FIGS. 3 and 4 below).

Cold rolling creates the unwanted effect of increasing the hardness ofthe steel. An annealing technique is therefore applied to the coils toreduce the hardness of the steel. For example, an annealing furnace maybe used to perform the annealing operation. Multiple coils may bestacked in the furnace with diffuser plates placed between the coils andan inner cover placed over the stack of coils. This apparatus then usesa base fan to circulate gases (e.g., nitrogen) around the coils and tothereby heat the coils by means of convection. This device may thenemploy water-filled tubes to cool the coils. The heating and cooling iscontrolled to ensure that the steel develops the desired mechanical andchemical properties.

A temper rolling procedure may be used to reduce hardness anomalies thatmay have formed in the strip of steel in the annealing process. Thistechnique may use an uncoiling reel, one or two stands that applypressure to the strip as it passes through the stands, and a tensionreel.

A galvanizing technique applies a coating to the steel to protect itfrom the environment (e.g., to protect it from rusting). Common coatingsinclude zinc, tin, chrome, or paint. A typical galvanizing techniqueuses multiple stages to apply the coating. For instance, a hot dipgalvanizing line may initially including heating the strip in a furnace.The strip is then partially cooled and passed through a bath of liquidzinc. Air jets remove excessive zinc from the surface of the metalstrip. Alternatively, an electrolytic galvanizing line involves passingthe strip through a series of electrolytic cells to apply the coating inwell-known electrolytic fashion. That is, the cells contain an acidsolution containing zinc. Current is passed through the strip, causingzinc ions to adhere to the strip.

As those skilled in the art will appreciate, yet additional principalsteps may be included in the production of steel to accommodateparticular applications and mill environments. Such addition steps mayinclude, but are not limited to, skin pass rolling, slitting operations,shear operations, continuous annealing lines, cut to length operations,etc.

Returning to FIG. 2, this figure also shows that sensors 218-228 areinterspersed throughout the process 102. As indicated there, some ofthese sensors (e.g., sensor 218) may monitor the performance of asubprocess at some intermediary stage in the subprocess. Other sensors(e.g., sensor 224) may measure the quality of a final product as it isoutput from a subprocess. A communication line (or lines) 230 receivesthe signals generated by the sensors and transfers this information toappropriate analysis equipment.

FIGS. 3 and 4 illustrate exemplary placement of tension and x-raysensors within a cold rolling mill, and the use of the sensors' outputto control the cold rolling operation. A typical mill environment willemploy both of the configurations shown in FIGS. 3 and 4. However, theconfigurations are separated in FIGS. 3 and 4 to facilitate explanation.

To begin with, FIG. 3 shows the tension-regulation aspects of theexemplary cold rolling processing. The cold rolling mill transfers astrip of steel 301 from uncoiling mechanism 302 to coiling mechanism 322through a series of stands 304 (S1), 306 (S2), 308 (S3), 310 (S4), and312 (S5). The series of stands apply pressure to the strip 301 andprogressively reduce its thickness. Each stand comprises a conventionalconfiguration, comprising a top to bottom arrangement that includes atop backing roll, a top working roll, a bottom working roll, and abottom or lower backing roll. For example, stand 304 (S1) includes a topbacking roll 314, a top working roll 316, a bottom working roll 318, anda bottom backing roll 320. The working rolls define a gap for receivingand compressing the strip 301 as it passes through the gap. The gap insuccessive stands may become progressively more narrow to achieve thedesired thickness reduction in a stepwise fashion.

The mill 300 includes a plurality of tension measuring sensorspositioned at various points in the progress of the strip. Namely, afirst sensor 328 (T12) is positioned between stands S1 and S2. A secondsensor 330 (T23) is positioned between stands S2 and S3. A third sensor332 (T34) is positioned between stands S3 and S4. A fourth sensor 334(T45) is positioned between stands S4 and S5. These tension sensors maycomprise load cells positioned underneath the strip. The weight placedon the cells is related to the tension between the stands, which may becomputed using a trigonometric function.

Equipment 336 generically represents the controllers, drives, etc. usedto operate the mill. For example, the equipment 336 may include aplurality of hydraulic force regulators used to govern the force appliedto respective stands (e.g., as indicated by exemplary control coupling324). The equipment 336 may further include a plurality of speedregulators used to govern the speed of the stands in conventionalfashion (e.g., as indicated by exemplary control coupling 326). Aplurality of tension regulator devices may receive tension measurementsfrom the respective tension sensors and provide output to the hydraulicforce regulators and the speed regulators in a conventional fashion.

More specifically, the control logic contained in equipment 336 attemptsto maintain the tensions between the stands at a constant level. Thereare two ways of achieving this objective. According to one technique,the equipment's speed regulators adjust the speed of one stand relativeto its adjacent stands. For instance, the equipment 336 may speed upstand 308 (S3) relative to stand 306 (S2). The combined effect is tomore tightly pull the strip between these two stands. A preferred way ofadjusting tension is to change the gaps between the stands' workingrolls. To implement this technique, the tension regulators receiveinformation provided by respective tension sensors. Based on thisinformation, the tension regulators provide commands to the hydraulicforce regulators; these commands instruct the hydraulic force regulatorsto change the gaps between the working rolls of the respective stands.

FIG. 4 shows another representation of the cold rolling mill thatillustrates the use of x-ray sensor data to control the operation of themill. As discussed above, the cold rolling mill transfers a strip ofsteel 401 from uncoiling mechanism 402 to coiling mechanism 422 througha series of stands 404 (S1), 406 (S2), 408 (S3), 410 (S4), and 412 (S4).Stand 404 (S1) includes a top backing roll 414, a top working roll 416,a bottom working roll 418, and a bottom backing roll 420. The otherstands include a similar arrangement of rolls.

The cold rolling mill also includes a plurality of x-ray sensorsinterspersed throughout the mill. Namely, a first x-ray sensor 426 (X0)is positioned prior to the first rolling stand 404. A second x-raysensor 428 (X1) is positioned between the first and second rollingstands (404, 406). A third x-ray sensor 430 (X2) is positioned betweenthe second and third rolling stands (406, 408). A fifth x-ray sensor 432(X5) is positioned after the fifth rolling stand 412. These x-raysensors measure the thickness of the strip by projecting x-rayelectromagnetic radiation through the strip, and sensing the strength ofradiation which passes through the strip.

Equipment 436 generically represents the controllers, drives, etc. usedto operate the mill. For example, the equipment 436 may include aplurality of hydraulic force actuators/regulators used to govern theforce applied to respective stands (e.g., as indicated by the exemplarycontrol coupling 424). The equipment 436 may further include a pluralityof speed regulators used to govern the speed of the stands inconventional fashion (e.g. as indicated by the exemplary controlcoupling 426). The equipment 436 may further include a plurality ofautomatic gauge control (AGC) devices for controlling the operation ofthe hydraulic force regulators and speed regulators on the basis of theoutput of the x-ray sensors. This has the effect of increasing ordecreasing the thickness of the strip which exits the last stand (412)of the cold rolling mill, thereby, maintaining the thickness within theprescribed tolerances.

In one embodiment, one of the automatic gauge control (AGC) deviceswithin equipment 436 receives the input from the last x-ray sensor 432(X5). The outgoing thickness of the strip is measured as a function ofthe output of this x-ray sensor, and based on this measurement, theequipment 436 may make appropriate adjustments to the operation of thecold rolling mill (e.g., by increasing or decreasing the speed of thestand rolls). This adjustment mechanism therefore operates based on afeedback control model. Another automatic gauge control device mayreceive the input from the first x-ray sensor 426 (X0). The incomingthickness of the strip is derived based on the output of this x-raysensor, and based on this measurement, the controller makes appropriateadjustments to the operation of the cold rolling mill. This adjustmentmechanism therefore operates based on a feedforward control model. Inaddition, other automatic gauge control devices may receive the outputsof sensors 428 and 430. Based on these outputs, the gauge controldevices may make adjustments to the cold rolling process to helpstabilize the mass flow rate of the metal being processed by the mill.Generally speaking, the equipment 436 attempts to maintain the mass flowrate and thickness of processed steel at a constant level.

In the context of FIGS. 3 and 4, the control model used to control thecold rolling operation may comprise various reference points that definespeed settings, tensions settings, gauge settings, etc. The settingsdefine starting points for the various regulators used in thecontrollers shown in FIGS. 3 and 4. Different recipes may be appropriatefor processing different classes of steel. As mentioned above, a remotecomputer, such as the remote system 110 shown in FIG. 1, supplies therecipes that define these reference points.

FIG. 5 shows exemplary logic 500 for analyzing anomalies based on theoutput of the sensors. Such logic may, for instance, be implemented asthe software functionality 122 shown in FIG. 1. In one embodiment, thelogic 500 analyzes the sensor output from one principal step in theprocess. FIG. 5 illustrates this embodiment by showing the sensor outputfor a subprocess 510 being fed into the logic 500. In anotherembodiment, the logic 500 analyzes the sensor output from plural stepsin the process. FIG. 5 illustrates this embodiment by showing the sensoroutput for plural subprocesses 508 being fed into the logic 500.

The logic 500 includes a parameter extractor 502. The parameterextractor 502 examines the characteristics of the sensor output, andthen extracts one or more parameters that characterize the output. Thatis, the extractor generally examines a collection of data (such as aplurality of data points within a timeframe of data), and extracts oneor more parameters that capture the general characteristics of suchdata. Different calculation techniques may be used to perform thisextraction. In one embodiment, the logic 500 may compute an average,standard deviation, center of gravity, slope, etc. for use as extractedparameters. In another embodiment, the logic 500 may use anappropriately trained neural network to analyze the sensor output and togenerate one or more high-level parameters that characterize the data.In another embodiment, the logic 500 may convert the signal to adifferent processing domain to extract the parameters (e.g., byconverting the signal from the time domain to the frequency domain, orother domain). In another embodiment, the logic may perform a comparisonof the signal with pre-stored templates to extract the parameters, wherethe templates may be selected to identify distinguishing features in thesensor output, such as characteristic signal envelopes, dramatic changesin signal level, etc. Those skilled in the art, will appreciate thatother techniques for extracting parameters may be used to suitparticular manufacturing environments that give rise to characteristicsensor output.

A parameter knowledge base 504 stores reference information regardingtypical parameters that may be extracted by the parameter extractor 502.This knowledge base 504 also maps the stored parameters with anindication of the anomalies associated with the parameters.

A comparator/analyzer 506 receives the extracted parameters from theparameter extractor 502 and the reference information received fromknowledge base 504. The comparator/analyzer 506 then compares theextracted parameters with the previously stored entries in the knowledgebase 504. The comparator/analyzer 506 then generates an output whichindicates a diagnosis pertaining to matching parameters. That is, thecomparator/analyzer provides an indication whether the parametersextracted from the paramter extractor 502 match any of the referenceinformation stored in the knowledge base 504, and an indication of theanomaly(ies) associated therewith. The comparator/analyzer 506 may alsoprovide recommendations regarding steps that may be taken to remedy ananomalous condition associated with matching parameters, or may simplygenerate an appropriate alarm.

FIG. 6 shows a table for storing parameters that may be extracted by theparameter extractor 502. In a first level of analysis, thecomparator/analyzer 506 forms a diagnosis based on parameters associatedwith a single product (such as a single coil of metal or paper). Forexample, the comparator/analyzer 506 may examine the two parameters inset 604 associated with product No. 10, or the six parameters in set 602associated with product No. 11. In another embodiment, thecomparator/analyzer 506 may analyze one or more parameters extractedfrom multiple products to generate a diagnosis. For instance, thecomparator/analyzer 506 may analyze compilations 606 or 608 to generatea diagnosis. Compilation 606 includes a single parameter extracted fromproduct Nos. 11-15. This grouping permits analysis with respect to asingle batch of products. Compilation 608 includes another singleparameter extracted from product Nos. 1-15. This grouping permitsanalysis with respect to multiple batches of products. In still furtherembodiments (not shown), the comparator/analyzer 506 bases its diagnoseson yet further compilations of parameter sets, including compilationsthat include both intra-product samplings and inter-product samplings.

FIG. 7 provides an example of typical information extracted from some ofthe sensors used in a cold rolling mill. Signals presented using solidlines represent the direct time-trace output of sensors in the coldrolling mill. In contrast, dotted lines represent summary informationextracted from multiple products. That is, each dot that appears inthese lines may represent a respective value computed for a singleproduct. In the context of FIG. 6, such a compilation reflects avertical compilation of data (such as represented in sets 606 or 608).The summary data may be selected to best characterize the product, andmay comprise, for instance, an average value computed from sensor data,a standard deviation value computed from sensor data, etc.

Each of the signals is characteristic of a different kind of anomalypresent in the cold rolling process. To begin with, the first trace isdenoted by the caption “heads swing very lightly.” This phenomenonrefers to a situation in which the process controller converges onwithin-tolerance conditions for strip production in a problematicmanner. That is, a customer typically specifies desired stripcharacteristics, such as a strip thickness and permissible standarddeviation from this thickness. When the cold rolling process commences(e.g., upon feeding the “head end” of the strip through the series ofstands), the controller takes a finite amount of time to converge on thedesired strip characteristics. This finite amount of time is attributedto the fact that the controller needs sufficient time to collectmeasurements on the quality of strip produced, and to interactively makeappropriate adjustments to the process. The phenomenon “heads swing verylightly” refers to a condition in which the controller “zeros in” on thedesired characteristics in an undesirable manner.

FIG. 7 shows an exemplary time trace of the “heads swing very light”phenomenon. The signal represents the output of one of the x-raysensors, such as the X1 and/or X5 sensors shown in FIG. 4. In thisparticular case, the vertical axis represents thickness, where the zeroreference denotes a desired thickness specified by the customer. Thehorizontal axis represents time. As shown there, the signal starts highat the initial feeding of the head end. It then drops below zero, thenrises above zero, and then eventually converges on optimal levels. Theportion where the thickness drops below tolerance represents anundesirable condition, as it may result in the production of a finishedproduct having thin spots. By contrast, the preferred signalcharacteristic during the initial feeding operation (not shown) exhibitsa quick convergence on the zero condition, without too much overshoot orother deviation.

The above-described phenomenon is attributed to the controller runningbelow optimal levels. This cause encompasses any operational anomaliesexperienced by the controller.

The second signal characteristic shown in FIG. 7 is denoted by thecaption “bad set up, excessive forces.” This phenomenon refers to asituation in which the “set up” is not optimally suited for the grade. A“set up” defines a table of reference points used to control theoperation of the cold rolling process, such as reference points thatcontrol the operation of the speed regulators, hydraulic forceactuators, etc. A single table may be suitable for multiple grades ifthe grades have similar characteristics. The phenomenon “bad set up,excessive forces” refers to the use of a reference point table that isnot appropriate for a particular grade, e.g., based on inaccuratemapping between the grade and its associated table.

FIG. 7 shows an exemplary signal characteristic of the “bad set up,excessive forces” phenomenon. Each dot represents a sampling of modelfeedback forces for a single coil. Accordingly, the series of dots mayrepresent multiple coils in a batch (such as represented by coilgrouping 608 shown in FIG. 6). In this particular case, the verticalaxis represents model feedback forces. The horizontal axis representstime. As shown there, a first group of coils exhibits a first level offeedback forces. A second group of coils exhibits a second level offeedback forces. The difference between the first level of feedbackforces and the second level of feedback forces indicates that the firstand second grades represented by the two groups of coils may have beenimproperly grouped together. In other words, the two groups of coilsshould not have been mapped to the same reference point table.

As explained above, the above-described phenomenon is attributed to themismapping of model grades (that is, the improper linking of modelgrades to reference point tables).

FIG. 8 shows an exemplary routine for performing the model adjustmentdescribed above with reference to FIG. 1. The local processing site(e.g., at the manufacturing plant) performs the processing operationsdescribed on the left side of FIG. 8, while the remote processing site(e.g., the remote server 110) performs the processing operationsdescribed on the right side of FIG. 8.

In step 802, the local processing site collects sensor output from thecontrolled process 102. At step 804, the local site forwards this sensordata to the remote site. At step 806, the remote site analyzes thesensor data. Such analysis may consist, for example, of the processingdescribed previously in the context of FIGS. 5-7. That is, this processmay comprise extracting parameters from the measured sensor output, andcomparing those parameters with respect to a knowledge base ofpreviously stored parameter information. Alternatively, this process mayemploy some other type of analysis. Then, in step 808, the remote sitedetermines, on the basis of the analysis performed in step 806, whetheradjustment of the control model is appropriate. If so, in step 810, theremote site adjusts the control model and generates an output resultthat reflects this adjustment. If no adjustment is warranted, in step812, the remote site may optionally generate an output signal indicatingthat no adjustment is warranted. In step 814, the remote site transmitsthe above-described output result to the local site. In step 816, thelocal site receives the output result and modifies the control modelbased on instructions received from the remote site. The process is thenrepeated as the sensors used in the process collect additional sensordata. That is, the system may be configured such that the process shownin FIG. 8 is repeated at periodic intervals.

FIG. 9 is a flowchart that shows the extraction and analysis ofparameters from sensor signal data. In step 902, data from at least onesensor is received. In step 904, the sensor data is processed byextracted high-level parameters from this information in the mannerdescribed above. In step 906, the reference information is retrievedfrom the knowledge base. In step 908, the technique compares theextracted parameters with the reference information to reach conclusionsregarding potential anomalies in the process. In step 910, the techniquegenerates and outputs information that reflects its conclusionsregarding potential anomalies.

The analysis performed in step 908 may comprise two separate processingsteps. Namely, in step 912, the technique examines the parametersextracted from one of the subprocesses to diagnoses any failures thatmay have occurred in this subprocess. For instance, in step 912, thetechnique examines parameters extracted from a cold rolling operation todetermine whether any of the anomalies identified in FIG. 7 may haveoccurred. In addition, or alternatively, in step 914, the techniqueexamines the parameters extracted from multiple subprocesses todiagnoses any failures that may have occurred in or one or more of thesesubprocesses. Step 914 may also provide an indication of the subprocesswhere the anomaly originated from. The analysis performed in step 914may be based on similar principles to those identified with respect toFIGS. 5-7. Namely, the comparator/analyzer 506 may compare multipleparameters extracted from different subprocesses with respect topreviously stored parameter values to detect the cause and origin ofanomalies.

Although the invention was described above in the exemplary andillustrative context of steel production, it may be applied to othermanufacturing environments, such as paper production, etc.

Other modifications to the embodiments described above can be madewithout departing from the spirit and scope of the invention, as isintended to be encompassed by the following claims and their legalequivalents.

1. A system for analyzing an anomalous condition, comprising: a processfor producing a product, including: plural subprocesses for performingoperations on the product, wherein each subprocess includes at least oneactuator for controlling the respective subprocess, wherein eachsubprocess includes at least one sensor for measuring informationpertaining to the status of the respective subprocess, and forgenerating an output based thereon; a parameter extractor for, for eachof the subprocesses, receiving the output from the at least one sensor,and for generating at least one representative value that ischaracteristic of a pattern expressed in the output, the parameterextractor thus generating a plurality of representative values for theprocess as a whole; a knowledge base for storing data including aplurality of representative values, and also including information whichmaps the representative values to associated anomalous conditions; ananalyzer for analyzing the plurality of representative values outputfrom the parameter extractor with respect to the data stored in theknowledge base, and for generating a diagnostic result which diagnosesan anomalous condition in the process, and also identifies at least oneof the subprocesses which has caused the anomalous condition; andcontrol logic for using the diagnostic result to affect correctiveaction to the at least one the subprocesses which has caused theanomalous condition by adjusting at least one actuator that controls theat least one subprocess.
 2. The system of claim 1, wherein the processis for manufacturing metal, plastic extrusion or paper-based products.3. The system of claim 1, wherein the process is for manufacturing metalproducts, and the process includes the following subprocesses: a hotrolling subprocess for reducing the thickness of the metal products in aheated state; a pickling subprocess for removing unwanted material fromthe metal products; a cold rolling subprocess for reducing the thicknessof the metal products in a cold state using a plurality of rollingstands; and an annealing subprocess for heating and subsequently coolingthe metal product.
 4. The system of claim 1, wherein the analyzer isconfigured to provide a diagnosis based on samples taken from the atleast one sensor for one discrete product.
 5. The system of claim 1,wherein the analyzer is configured to generate summary values forrespective discrete products, and to provide a diagnosis based on thesummary values.
 6. A system for analyzing an anomalous condition in aprocess for producing a product, the process including pluralsubprocesses for performing operations on the product, wherein eachsubprocess includes at least one actuator for controlling the respectivesubprocess, and wherein each subprocess includes at least one sensor formeasuring information pertaining to the status of the respectivesubprocess, and for generating an output based thereon, comprising: aparameter extractor for, for each of the subprocesses, receiving theoutput from the at least one sensor, and for generating at least onerepresentative value that is characteristic of a pattern expressed inthe output, the parameter extractor thus generating a plurality ofrepresentative values for the process as a whole; a knowledge base forstoring data including a plurality of representative values, and alsoincluding information which maps the representative values to associatedanomalous conditions; an analyzer for analyzing the plurality ofrepresentative values output from the parameter extractor with respectto the data stored in the knowledge base, and for generating adiagnostic result which diagnoses an anomalous condition in the process,and also identifies at least one of the subprocesses which has causedthe anomalous condition.
 7. The system of claim 6, wherein the processis for manufacturing metal, plastic extrusion or paper-based products.8. The system of claim 6, wherein the process is for manufacturing metalproducts, and the process includes the following subprocesses: a hotrolling subprocess for reducing the thickness of the metal products in aheated state; a pickling subprocess for removing unwanted material fromthe metal products; a cold rolling subprocess for reducing the thicknessof the metal products in a cold state using a plurality of rollingstands; and an annealing subprocess for heating and subsequently coolingthe metal product.
 9. The system of claim 6, wherein the analyzer isconfigured to provide a diagnosis based on samples taken from the atleast one sensor for one discrete product.
 10. The system of claim 6,wherein the analyzer is configured to generate summary values forrespective discrete products, and to provide a diagnosis based on thesummary values.
 11. A method for analyzing an anomalous condition in aprocess for producing a product, the process including pluralsubprocesses for performing operations on the product, comprising: foreach of the subprocesses, providing sensor output from at least onesensor used to measure information pertaining to the status of therespective subprocess; for each of the subprocesses, extracting at leastone representative value that is characteristic of a pattern expressedin the output, thus generating a plurality of representative values forthe process as a whole; retrieving data from a knowledge base, the dataincluding a plurality of representative values, and also includinginformation which maps the representative values to associated anomalousconditions; analyzing the plurality of representative values output fromthe parameter extracting step with respect to the data stored in theknowledge base, and for generating a diagnostic result which diagnosesan anomalous condition in the process, and also identifies at least oneof the subprocesses which has caused the anomalous condition; and usingthe diagnostic result to affect corrective action to the at least one ofthe subprocesses which has caused the anomalous condition by adjustingat least one actuator that controls the at least one subprocess.
 12. Thesystem of claim 11, wherein the process is for manufacturing metal,plastic extrusion or paper-based products.
 13. The method of claim 11,wherein the process is for manufacturing metal products, and the processincludes the following subprocesses: a hot rolling subprocess forreducing the thickness of the metal products in a heated state; apickling subprocess for removing unwanted material from the metalproducts; a cold rolling subprocess for reducing the thickness of themetal products in a cold state using a plurality of rolling stands; andan annealing subprocess for heating and subsequently cooling the metalproducts.
 14. The method of claim 11, wherein the analyzing step isconfigured to provide a diagnosis based on samples taken from the atleast one sensor for one discrete product.
 15. The method of claim 11,wherein the analyzing step is configured to generate summary values forrespective discrete products, and to provide a diagnosis based on thesummary values.
 16. A method for analyzing an anomalous condition in aprocess for producing a product, the process including pluralsubprocesses for performing operations on the product, where each of thesubprocesses provides sensor output from at least one sensor used tomeasure information pertaining to the status of the respectivesubprocess, the method comprising the steps of: for each of thesubprocesses, extracting at least one representative value that ischaracteristic of a pattern expressed in the output of the at least onesensor, thus generating a plurality of representative values for theprocess as a whole; retrieving data from a knowledge base, the dataincluding a plurality of representative values, and also includinginformation which maps the representative values to associated anomalousconditions; and analyzing the plurality of representative values outputfrom the parameter extracting step with respect to the data stored inthe knowledge base, and for generating a diagnostic result whichdiagnoses an anomalous condition in the process, and also identifies atleast one of the subprocesses which has caused the anomalous condition.17. The method of claim 16, wherein the process is for manufacturingmetal, plastic extrusion or paper-based products.
 18. The method ofclaim 16, wherein the process is for manufacturing metal products, andthe process includes the following subprocesses: a hot rollingsubprocess for reducing the thickness of the metal products in a heatedstate; a pickling subprocess for removing unwanted material from themetal products; a cold rolling subprocess for reducing the thickness ofthe metal products in a cold state using a plurality of rolling stands;and an annealing subprocess for heating and subsequently cooling themetal products.
 19. The method of claim 17, wherein the analyzer isconfigured to provide a diagnosis based on samples taken from the atleast one sensor for one discrete product.
 20. The method of claim 16,wherein the analyzer is configured to generate summary values forrespective discrete products, and to provide a diagnosis based on thesummary values.